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NITRO: A Framework for Adaptive Code Variant Tuning

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1 NITRO: A Framework for Adaptive Code Variant Tuning
Saurav Muralidharan, Manu Shantharam, Mary Hall, Michael Garland*, Bryan Catanzaro* University of Utah and *NVIDIA Research

2 Disclaimers This research was funded in part by the U.S. Government. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Government. This research was funded by DARPA contract HR Co-authors of this paper own stock in NVIDIA Corporation

3 Motivation Some computations may have many implementations
Example: BFS, SpMV, Solvers, Sort etc. Performance of implementations may depend on input and architecture Set of implementations constitutes a ‘search space’ Best implementation may not be known till runtime This paper describes a framework that tries to dynamically select the best implementation

4 Sparse Matrix-Vector Multiplication
Sparse matrices represented using many formats Example formats: Compressed Sparse Row (CSR), DIA etc. Optimized implementations exist for each format Exploit as much structure of the matrix as possible Running Example: SpMV implementations in CUSP library DIA CSR-VEC ELL

5 Input Dependence in SpMV

6 Autotuning Systems Navigate a search space of:
Parameters Implementations, a.k.a ‘Code Variants’ Objective: Find the best ‘point’ in search space According to some optimization criteria Usually Performance Why autotuning?

7 Tuning Code Variants Parameter tuning systems
Can we tune variants using parameter tuning systems? How do we ‘prune’ the search space? Most information known only at runtime Do we run search heuristic on every execution of program? We need some sort of ‘model’ or mapping param_1 param_2 Search Space param_1 param_2 param_1: 5.0 Search Heuristic param_2: 3.5

8 Nitro: Introduction What is Nitro?
Goal: Provide general productivity tool for experts Both library and application developers Some Terminology Model: Feature: Characteristic or property of input data Constraint: A check to prevent execution of invalid variant Programmer-directed code variant tuning framework Infers mapping: inputs  variants Uses mapping to select runtime Input features Variant label

9 Tuning Process Overview
Library Driver (C++) Tuning Script (Python) Training Inputs Active Learner Feature Evaluator Nitro Tuning Subsystem Classifier Constraint Evaluator Models

10 my_lib::SpMV(matrix);
Nitro Production Use User Library (my_lib) Nitro Library SpMV (...) CSR_VEC DIA ELL ... F1 F2 Fj C1 C2 Ck SpMV (...) CSR_VEC DIA ELL ... F1 F2 Fj C1 C2 Ck my_lib::SpMV(matrix); DIA Run DIA Query Models SpMV Model End User User Library

11 SpMV Library Driver (C++)
// Create Nitro tuning context context cx; ... code_variant<tuning_policies::spmv, ArgTuple> spmv(cx); // Declare and add variants csr_vector_type<T> csr_vector_variant; dia_type<T> dia_variant; spmv.add_variant(&csr_vector_variant); spmv.add_variant(&dia_variant); Auto-Generated from Tuning Script thrust::tuple of Variant Args C++ Functor Containing DIA Variant

12 SpMV Library Driver (C++)
// Declare and add features... avg_nnz_per_row_type<T> avg_nnz_feature; ... spmv.add_input_feature(&avg_nnz_feature); // ... and constraints dia_cutoff_type dia_cutoff; spmv.add_constraint(&dia_cutoff); // Call variant spmv(input_matrix); Padding estimate for conversion to DIA Format

13 SpMV Tuning Script (Python)
# Provide application, fn name, number of variants tuner = autotuner(“spmv”) spmv = code_variant(“spmv”, 6) # Set variant-specific tuning options spmv.classifier = svm_classifier() spmv.constraints = True # Provide training data for classifier tuner.set_training_args(input) # Perform autotuning of variant tuner.tune([spmv])

14 Feature & Constraint Evaluation
Model Construction Tuning subsystem builds a model that maps a given feature vector to label corresponding to optimal variant Offline training phase Plug-in support for classifiers Support Vector Machines (using libSVM) is currently used by default: RBF Kernel is default; parameters found using cross-validation based parameter search DIA CSRV Labeled Training Data Training Inputs Exhaustive Search Feature & Constraint Evaluation

15 Improving Training & Runtime Overheads
Incremental tuning through Active Learning Parallel feature and constraint evaluation Asynchronous feature function execution Training Pool Active Pool BvSB Pick Retrain Model

16 Experimental Setup Target architecture: Tesla C2050 (Fermi)
Training inputs Taken from standard sets Exemplar input for each variant (minimally) Test inputs Distinct from training data Test set much larger than training set to test generalization

17 Benchmarks Features specific to each benchmark; details in paper
Variants SpMV (CUSP) CSR Scalar (Tex/Non-Tex) CSR Vector (Tex/Non-Tex), ELL, DIA Pre-Conditioner+Solver (CULA) (CG, BiCGStab) Solvers (Jacobi, Blocked Jacobi, FAInv) Pre-conditioners BFS (Back40Computing) E-C (Fused/Iterative) C-E (Fused/Iterative) 2-Phase (Fused/Iterative) Histogram (CUB) (Sort, Global-Atomic, Shared-Atomic) Variants (Even-Share, Dynamic) Grid Mappings GPU Sort (CUB, ModernGPU) Merge, Locality, Radix Features specific to each benchmark; details in paper

18 Results: Nitro vs. Other Variants
On average, Nitro achieves at least 93% performance w.r.t exhaustive search

19 Performance Breakdown
~ 80% of test set achieves at least 90% of performance.

20 Results: Incremental Tuning
Achieves 90% of performance of full training set in ~ 25 iterations

21 Related Work Variant Tuning Systems: PetaBricks, STAPL etc.
Tuning based on general input characteristics Parameter Tuning Systems: Active Harmony, Orio etc. Domain-Specific Autotuners: OSKI, SPIRAL, etc. Other Solutions to Algorithm Selection Problem MDP, Reinforcement Learning etc. Can be integrated into Nitro’s learning sub-system

22 Conclusions & Future Work
Nitro Programmer-directed code variant tuning system Uses supervised learning to select variants based on input dataset features For 5 high-performance GPU benchmarks, Nitro-tuned variants achieve over 93% of performance w.r.t exhaustive search Incremental tuning supported via Active Learning Future Work Automatic variant generation from high-level specifications Architectural features & features derived from compiler analysis Tunable parameter support


24 Feature Evaluation Overhead
Analysis helps remove features with high asymptotic complexity

25 Library and Tuning Interfaces

26 Benchmarks: Features Sparse Matrix-Vector Multiplication
AvgNZPerRow, RL-SD, MaxDeviation, DIA and ELL Fillin Pre-conditioner + Solvers NNZ, #Rows, Trace, DiagAvg, DiagVar, DiagDominance, LBw, Norm1 Breadth-First Search AvgOutDeg, Deg-SD, MaxDeviation, #Vertices, #Edges Histogram N, N/#Bins, SubSampleSD GPU Sort N, #Bits, #AscSeq

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